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Bayesian epidemic models for spatially aggregated count data

Author

Listed:
  • Malesios, C
  • Demiris, N
  • Kalogeropoulos, K
  • Ntzoufras, I

Abstract

Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so we utilise a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time varying covariates through an Ornstein Uhlenbeck process formulation. In addition, we explore the effect of different priors, including default options and variations of mixtures of g-priors. The effect of different distance kernels for the epidemic model component is investigated. We proceed by developing branching process-based methods for testing scenarios for disease control, thus linking traditional epidemiological models with stochastic epidemic processes, useful in policy-focused decision making. The approach is illustrated with an application to a sheep pox dataset from the Evros region, Greece.

Suggested Citation

  • Malesios, C & Demiris, N & Kalogeropoulos, K & Ntzoufras, I, 2017. "Bayesian epidemic models for spatially aggregated count data," LSE Research Online Documents on Economics 77939, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:77939
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    File URL: http://eprints.lse.ac.uk/77939/
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    References listed on IDEAS

    as
    1. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    2. Young Ku Choi & Wesley O. Johnson & Geoff Jones & Andres Perez & Mark C. Thurmond, 2012. "Modelling and predicting temporal frequency of foot‐and‐mouth disease cases in countries with endemic foot‐and‐mouth disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 619-636, April.
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    More about this item

    Keywords

    Bayesian modelling; Bayesian variable selection; branching process; epidemic extinction; g-prior; spatial kernel; disease control;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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